AttributeError: 'Index' object has no attribute 'contains'

AttributeError是一个常见的Python错误，它发生在试图访问一个对象的属性或方法时，但该对象实际上并没有这个属性或方法。在这个特定的错误中，"Index"对象指代的是Pandas库中的索引(Index)对象，`contains`不是一个Index对象应有的属性。通常，`contains`方法用于检查序列（如列表、字符串等）是否包含某个值。如果你在一个Index对象上调用了`contains`，可能是你需要检查的数据结构不是正确的，或者是API的使用不恰当。

例如，如果你想查找一个DataFrame中列名是否包含特定的子串，应该使用`.loc`或`.iloc`方法，而不是直接对Index操作：

df = pd.DataFrame()
# 错误的尝试
index_contains_error = df.index.contains('some_substring')  # 这里会抛出AttributeError

# 正确的方式
correct_check = 'some_substring' in df.columns  # 或者
correct_check = df.columns.str.contains('some_substring')

相关问题

AttributeError: DataFrame object has no attribute append . Did you mean: _append ?

This error occurs when you try to call the `append` method on a Pandas DataFrame object, but the object does not have an `append` attribute.

One possible reason for this error is that you are trying to append a DataFrame to another DataFrame using the `append` method, but you are not using it correctly. In Pandas, the `append` method does not modify the original DataFrame, but instead it returns a new DataFrame that contains the rows from both DataFrames. Therefore, you need to assign the result of the `append` method to a new variable or to the original DataFrame, like this:

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
df3 = df1.append(df2)

In this example, `df3` will contain the rows from both `df1` and `df2`.

If you are still getting the AttributeError, it's possible that the DataFrame object you are trying to append to does not have an `append` attribute. In this case, you can try using the `_append` attribute instead, which is a private method that is used internally by Pandas. However, be aware that using private methods may not be supported in future versions of Pandas and may cause unexpected behavior.

df1._append(df2)

AttributeError: 'Index' object has no attribute 'loc'

在给定的代码中，报错"AttributeError: 'Index' object has no attribute 'loc'"是因为在使用data.loc时，data被设置为了索引列'No'，而不是DataFrame。因此，出现了该错误。

要解决这个问题，你可以将data重置为DataFrame，然后使用.loc进行筛选操作。你可以按照以下步骤进行操作：

1. 首先，将data重新设置为DataFrame，通过使用data.reset_index()函数。
2. 然后，使用.loc进行筛选操作，按照你的要求查找'No'列中包含'1'、'interpreted age'列小于1000的行，即：data.loc[(data['No'].astype(str).str.contains('1')) & (data['interpreted age']<1000), :]
3. 最后，打印出符合条件的结果。